Reasoning about temporal and spatial information is a common task in computer science, especially in the field of artificial intelligence. The topic of this thesis is the study of such reasoning from a computational perspective. We study a number of different qualitative point based formalisms for temporal reasoning and provide a complete classification of computational tractability for different time models. We also develop more general methods which can be used for proving tractability and intractability of other relational algebras. Even though most of the thesis pertains to qualitative reasoning the methods employed here can also be used for quantitative reasoning. For instance, we introduce a tractable and useful extension to the quantitative point based formalism STP. This extension gives the algebra an expressibility which subsumes the largest tractable fragment of the augmented interval algebra and has a faster and simpler algorithm for deciding consistency. The use of disjunctions in temporal formalisms is of great interest not only since disjunctions are a key element in different logics but also since the expressibility can be greatly enhanced in this way. If we allow arbitrary disjunctions, the problems under consideration typically become intractable and methods to identify tractable fragments of disjunctive formalisms are therefore useful. One such method is to use the independence property. We present an automatic method for deciding this property for many relational algebras. Furthermore, we show how this concept can not only be used for deciding tractability of sets of relations but also to demonstrate intractability of relations not having this property. Together with other methods for making total classifications of tractability this goes a long way towards easing the task of classifying and understanding relational algebras. The tractable fragments of relational algebras are sometimes not expressive enough to model real-world problems and a backtracking solver is needed. For these cases we identify another property among relations which can be used to aid general backtracking based solvers to finnd solutions faster. / Article I is a revised and extended version of the following three papers: 1. Mathias Broxvall and Peter Jonsson. Towards a Complete Classification of Tractability in Point Algebras for Nonlinear Time. In Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming (CP-99), pp. 129-143, Alexandria, VA, USA, Oct, 1999. 2. Mathias Broxvall and Peter Jonsson. Disjunctive Temporal Reasoning in Partially Ordered Time Structures. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), pp. 464-469, Austin, Texas, USA, Aug, 2000. 3. Mathias Broxvall. The Point Algebra for Branching Time Revisited. In Proceedings of the Joint German/Austrian Conference on Artificial Intelligence (KI-2001), pp. 106-121, Vienna, Austria, Sep, 2001. --- Article II is a revised and extended version of the following paper: Mathias Broxvall, Peter Jonsson and Jochen Renz: Refinements and Independence: A Simple Method for Identifying Tractable Disjunctive Constraints. In Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming (CP-2000), pp. 114-127, Singapore, Sep, 2000.

Object detection is a prominent area of research within computer vision. While object detection based on infrared videos holds great practical significance, the majority of mainstream methods are primarily designed for visible datasets. This thesis investigates the enhancement of object detection accuracy on infrared datasets by leveraging temporal and spatial information. The Memory Enhanced Global-Local Aggregation (MEGA) framework is chosen as a baseline due to its capability to incorporate both forms of information. Based on the initial visualization result from the infrared dataset, CAMEL, the noisy characteristic of the infrared dataset is further explored. Through comprehensive experiments, the impact of temporal and spatial information is examined, revealing that spatial information holds a detrimental effect, while temporal information could be used to improve model performance. Moreover, an innovative Dual Frame Average Aggregation (DFAA) framework is introduced to address challenges related to object overlapping and appearance changes. This framework processes two global frames in parallel and in an organized manner, showing an improvement from the original configuration. / Objektdetektion är ett framträdande forskningsområde inom datorseende. Även om objektdetektering baserad på infraröda videor har stor praktisk betydelse, är majoriteten av vanliga metoder i första hand utformade för synliga datauppsättningar. Denna avhandling undersöker förbättringen av objektdetektionsnoggrannhet på infraröda datauppsättningar genom att utnyttja tids- och rumslig information. Memory Enhanced Global-Local Aggregation (MEGA)-ramverket väljs som baslinje på grund av dess förmåga att införliva båda formerna av information. Baserat på det initiala visualiseringsresultatet från den infraröda datamängden, CAMEL, utforskas den brusiga karaktäristiken för den infraröda datamängden ytterligare. Genom omfattande experiment undersöks effekten av tids- och rumslig information, vilket avslöjar att den rumsliga informationen har en skadlig effekt, medan tidsinformation kan användas för att förbättra modellens prestanda. Dessutom introduceras en innovativ Dual Frame Average Aggregation (DFAA) ramverk för att hantera utmaningar relaterade till objektöverlappning och utseendeförändringar. Detta ramverk bearbetar två globala ramar parallellt och på ett organiserat sätt, vilket visar en förbättring från den ursprungliga konfigurationen.